Designing and Deploying Autonomous Multi-Agent AI SystemsPublished 9/2026
Created by Purankumar Gajera
MP4 |
Video: h264, 3840x2160 |
Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels |
Genre: eLearning |
Language: English |
Duration: 16 Lectures ( 9h 13m ) |
Size: 21.3 GB
⚡ Explain the core principles of Agentic AI and single-agent system design, including autonomy, reasoning, planning, and task execution.
⚡ Identify when and why to use Multi-Agent Systems (MAS) instead of a single-agent architecture for complex AI applications.
⚡ Design Multi-Agent System architectures using centralized, decentralized, hierarchical, heterarchical, and hybrid approaches.
⚡ Analyze agent roles, responsibilities, coordination, and system dynamics within a multi-agent architecture.
Requirements❗ No prior experience with Multi-Agent Systems is required. Students should have a basic understanding of Artificial Intelligence and Large Language Models (LLMs), but advanced AI or programming experience is not necessary.
Description"This course contains the use of artificial intelligence."
Welcome to
Architecting Agentic AI: Design and Deployment of Multi-Agent Systems-a technical course designed to help you understand how intelligent AI agents can be organized, coordinated, and architected into powerful Multi-Agent Systems (MAS).
As AI systems become more capable and autonomous, many complex problems cannot be effectively handled by a single agent alone. Multi-Agent Systems provide a way to divide responsibilities among specialized agents, coordinate their activities, enable communication, and create scalable architectures for solving complex tasks.
In this course, you will begin with the
core principles of single-agent design and understand the foundation on which more advanced Agentic AI architectures are built. You will then explore why organizations may move from a single-agent approach toward Multi-Agent Systems and how multiple autonomous agents can work together as a coordinated system.
You will learn the
anatomy of a Multi-Agent System, including agent roles, responsibilities, coordination, communication, autonomy, and system-level interactions. The course then examines different architectural approaches, including
centralized control, decentralized autonomy, hierarchical systems, heterarchical systems, and hybrid architectures.
A major focus of the course is understanding how agents communicate and share information. You will explore important communication paradigms such as
message passing and the
blackboard/shared-ledger approach, and understand how these choices influence the overall system architecture.
You will also learn how to evaluate different Multi-Agent System architectures and select an appropriate architecture based on factors such as
control, coordination, communication, autonomy, scalability, and system requirementsWhether you are an
AI engineer, software developer, system architect, solution architect, technical professional, student, researcher, or AI enthusiast, this course provides a structured foundation for understanding and designing modern Agentic AI architectures.
By the end of the course, you will have a clearer understanding of how to move from
single-agent thinking to multi-agent system architecture and how to develop a structured architectural blueprint for intelligent, coordinated, and scalable AI systems.
If you are ready to understand the architecture behind the next generation of autonomous AI systems,
enroll now and start your journey into Agentic AI and Multi-Agent Systems.Who this course is for⭐ This course is designed for AI engineers, software developers, solution architects, system architects, and technical professionals who want to understand how to design and deploy Agentic AI using Multi-Agent Systems.
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